Working Paper
MOSAIC (Modern Ocean Sediment Archive and Inventory of
Carbon): A (radio)carbon-centric database for seafloor surficial sediments
Author(s):
van der Voort, Tessa S.; Blattmann, Thomas M.; Usman, Muhammed; Montluçon, Daniel; Loeffler, Thomas;
Tavagna, Maria L; Gruber, Nicolas; Eglinton, Timothy I.
Publication Date:
2020-11-14 Permanent Link:
https://doi.org/10.3929/ethz-b-000456975
Originally published in:
Earth System Science Data Discussions , http://doi.org/10.5194/essd-2020-199
Rights / License:
Creative Commons Attribution 4.0 International
This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.
MOSAIC (Modern Ocean Sediment Archive and Inventory of Carbon):
1
A (radio)carbon-centric database for seafloor surficial sediments
2 3
Tessa Sophia van der Voort1, †, Thomas M. Blattmann1, ††, Muhammed Usman1, †††, Daniel 4
Montluçon1, Thomas Loeffler1, Maria Luisa Tavagna1, Nicolas Gruber2, and Timothy Ian 5
Eglinton1 6
7
1Department of Earth Sciences, Geological Institute, ETH Zürich, Sonneggstrasse 5, 8092 8
Zürich, Switzerland 9
2Department of Environmental System Sciences, Institute of Biogeochemistry and Pollutant 10
Dynamics, ETH Zürich, Universitätstrasse 16, 8092 Zürich, Switzerland 11
† New address: Campus Fryslân, University of Groningen, Wirdumerdijk 34, Leeuwarden 12
†† New address: Biogeochemistry Research Center, Japan Agency for Marine-Earth Science 13
and Technology (JAMSTEC), Yokosuka, Japan.
14
††† New address: Dept. of physical and environmental Sciences, University of Toronto M1CA4 15
Ontario, Canada 16
Journal: ESSD- Earth System Science Data 17
18
Key points paper:
19
(1) Paper presents global database for marine surficial sediments 20
(2) Database has a user-friendly interactive app with downloadable data 21
(3) Provides a new platform to answer key questions in biogeochemistry 22
23
Key words:
24
Ocean Sediments, Organic Carbon, Radiocarbon, 13C, Carbon Sequestration, MOSAIC, 25
Database 26
27
Abstract 28
Mapping the biogeochemical characteristics of surficial ocean sediments is crucial for 29
advancing our understanding of global element cycling, as well as for assessment of the 30
potential footprint of environmental change. Despite their importance as long-term repositories 31
for biogenic materials produced in the ocean and delivered from the continents, 32
biogeochemical signatures in ocean sediments remain poorly delineated. Here, we introduce 33
MOSAIC (Modern Ocean Sediment Archive and Inventory of Carbon; DOI:
34
https://doi.org/10.5168/mosaic019.1, mosaic.ethz.ch, Van der Voort et al., 2019), a 35
(radio)carbon-centric database that seeks to address this information void. The goal of this 36
nascent database is to provide a platform for development of regional to global-scale 37
perspectives on the source, abundance and composition of organic matter in marine surface 38
sediments, and to explore links between spatial variability in these characteristics and 39
biological and depositional processes. The database has a continental margin-centric focus 40
given both the importance and complexity of continental margins as sites of organic matter 41
burial. It places emphasis on radiocarbon as an underutilized yet powerful tracer and 42
chronometer of carbon cycle processes, and with a view to complementing radiocarbon 43
databases for other earth system compartments. The database infrastructure and interactive 44
web-application are openly accessible and designed to facilitate further expansion of the 45
database. Examples are presented to illustrate large-scale variabilities in bulk carbon properties 46
that emerge from the present data compilation.
47
48
1. Introduction 49
Oceans sediments constitute the largest and ultimate long-term global organic carbon (OC) 50
sink (Hedges and Keil, 1995), and serve as a key interface between short- and long-term 51
components of the global carbon cycle (Galvez et al., 2020). Assessments of the distribution 52
and composition of OC in ocean sediments are crucial for constraining carbon burial fluxes, 53
the role of ocean sediments in global biogeochemical cycles, and in interpretation of 54
sedimentary records. Constraining the magnitude of carbon stocks, as well as delineating the 55
sources, pathways and timescales of carbon transfer between different reservoirs (e.g., 56
atmosphere, oceanic water column, continents) comprise essential challenges. In this regard, 57
radiocarbon provides key information on carbon sources and temporal dynamics of carbon 58
exchange. The half-life of radiocarbon is compatible with assessments of carbon turnover and 59
transport times within and between different compartments of the carbon cycle, while also 60
serving to delineate shorter-term (< 50 kyr) and longer-term (> 50 kyr) cycles. Moreover, the 61
advent of nuclear weapons testing in the mid 20th century serves as a time marker for the onset 62
of the Anthropocene (Turney et al., 2018), and a tracer for carbon that has recently been in 63
communication with the atmosphere. With on-going dilution of this atmospheric “bomb spike”
64
with radiocarbon-free carbon dioxide from the combustion of fossil fuels (Graven, 2015; Suess, 65
1955), radiocarbon serves a particularly sensitive sentinel of carbon cycle change.
66 67
Radiocarbon databases or data collections have been established for the atmosphere (e.g.
68
University Heidelberg Radiocarbon Laboratory, 2020), ocean waters (Global Data Analysis 69
Project (GLODAP), Key et al., 2004), and most recently soils (ISRaD; Lawrence et al., 2020) 70
, with tree-rings, corals and other annually-resolved archives providing information on 71
historical variations in 14C in the atmosphere and surface reservoirs (Friedrich et al., 2020;
72
Reimer et al., 2009). At present, no such radiocarbon database exists for OC residing in ocean 73
sediments. As a sensitive tracer of carbon sources and carbon cycle perturbations, there is a 74
clear imperative to fill this information void given that on-going anthropogenic activities 75
directly and indirectly influence ocean sediment and resident OC stocks (Bauer et al., 2013;
76
Breitburg et al., 2018; Ciais et al., 2013; Keil, 2017; Regnier et al., 2013; Syvitski et al., 2003).
77
Materials accumulating in modern ocean sediments also provide a crucial window into how 78
on-going processes that are observable through direct instrumental measurements and remote 79
sensing data manifest themselves in the sedimentary record.
80
81
Over 85% of OC burial in the modern oceans occurs on continental margins, with deltaic, fjord 82
and other shelf and slope depositional settings constituting localized hotspots for carbon burial 83
(Bianchi et al., 2018; Hedges and Keil, 1995) . As the interface between land and ocean, 84
continental margins comprise a key juncture in the carbon cycle (Bianchi et al., 2018), provide 85
crucial habitats for unique marine ecosystems (Levin and Sibuet, 2012), support a major 86
fraction of the worlds fisheries (Worm et al., 2006), and participate in exchange processes with 87
the interior ocean (Dunne et al., 2007; Jahnke, 1996; Rowe et al., 1994). These ocean settings 88
and their underlying sediments are also amongst those most vulnerable to change (Keil, 2017) 89
through direct perturbations such as contaminant and nutrient discharge from land, loci of 90
intense resource extraction such as bottom trawling (Pusceddu et al., 2014) and mineral and 91
hydrocarbon recovery (e.g., Chanton et al., 2015), as well as indirect effects such as ocean 92
warming (Roemmich et al., 2012), acidification (Feely et al., 2008; Orr et al., 2005) and local 93
or large-scale deoxygenation (Diaz and Rosenberg, 2008; Keeling et al., 2010). Such influences 94
may change not only the amount of carbon sequestered in marine sediments but also its 95
character, with radiocarbon serving as a key metric to detect such change.
96 97
At present, an information gap exists between the numerous in-depth biogeochemical 98
investigations of carbon burial focused on geographically-localized regions (e.g. Bao et al., 99
2016; Bianchi, 2011; Castanha et al., 2008; Kao et al., 2014; Schmidt et al., 2010; Schreiner et 100
al., 2013) and global-scale syntheses that draw upon large suites of bulk OC concentration 101
measurements but are limited in diversity of geochemical information (e.g. Atwood et al., 102
2020; Premuzic et al., 1982; Seiter et al., 2004, 2005) and lack sedimentological context.
103
Consequently, current global-scale budgets and global-scale Earth System Models (ESMs) do 104
not resolve regional or small-scale variability (Bauer et al., 2013), and are limited by our 105
current understanding of variability in biogeochemical and sedimentary processes that 106
influence sedimentary organic matter composition and reactivity (Levin & Sibuet, 2012; Bao 107
et al., 2018; Arndt et al., 2013). Increasingly powerful Region Oceanic Model Systems 108
(ROMS) models (e.g., Gruber et al., 2012) and statistical methods for geospatial analysis (e.g., 109
van der Voort et al., 2018; Atwood et al., 2020) hold the potential to utilize information from 110
local-scale studies and inform ESMs, but these require mining and collation of existing data 111
and merging this with new observations. Spatially-resolved datasets for marine sedimentary 112
OC are beginning to emerge (e.g. Inthorn et al., 2006; Schmidt et al., 2010), including 113
radiocarbon measurements (e.g., Bao et al., 2016; Bosman et al., 2020). The latter information 114
is likely to increase in availability with the advent of natural-abundance C measurement via 115
elemental analysis coupled with gas-accepting accelerator mass spectrometry (AMS) systems 116
(McIntyre et al., 2016; Wacker et al., 2010) that enable routine, high-throughput 14C 117
measurements.
118 119
Overall, there is a strong need to synthesize information related to not only OC content, but 120
also its composition and depositional context, from separate region-based studies. Merging of 121
this information to provide pan-continental margin ocean floor data resources would enable 122
development of robust budgets and detection in changes in the magnitude or nature of carbon 123
stocks. In addition to the content and radiocarbon characteristics of OC that are of value in 124
constraining the provenance and reactivity of OM (Griffith et al., 2010), other geochemical 125
characteristics of organic matter, including the elemental composition (e.g., C/N ratio) 126
abundance, stable isotopic (13C, 15N) and molecular (biomarker) composition of organic matter, 127
as well as contextual properties such as sedimentation rate, mixed-layer depth, and redox 128
conditions (Aller and Blair, 2006; Arndt et al., 2013; Griffith et al., 2010) are needed to provide 129
a holistic depositional perspective. With on-going analytical advances that facilitate more 130
rapid and streamlined sediment analysis, it is anticipated that there will be substantial increases 131
in data availability and diversity, highlighting the urgent need to compile, organize and 132
harmonize existing datasets.
133 134
2. The MOSAIC database 135
In this study, we present MOSAIC (Modern Ocean Sediment Archive and Inventory of Carbon) 136
– a database designed to provide a window into the spatial variability in geochemical and 137
sedimentological characteristics of surficial ocean sediments on regional to global scales.
138
MOSAIC represents the starting point of an on-going endeavor to compile from data from prior 139
and on-going studies in order to build a comprehensive, continental margin-centric picture of 140
the distribution and characteristics of organic matter accumulating in modern ocean sediments.
141
The database infrastructure has been configured for facile incorporation of new data, for 142
expansion of included parameters, as well as for retrieval of data in an accessible and citable 143
format. MOSAIC is realized in an interactive web environment which allows users to visualize, 144
select and download data. This infrastructure is built using open-source (or optional open- 145
source) software (SI Table 1). The overarching goal is for MOSAIC to serve as a data platform 146
for the scientific community to explore the nature and causes of spatial patterns of 147
biogeochemical signatures in ocean sediments.
148 149
2.1. Database scope and content 150
151
2.1.1. Spatial and depth coverage and georeferencing 152
The focus of MOSAIC is on the coastal ocean (continental margins) with limited inclusion of 153
data from deep ocean settings. Attention is also restricted to surficial sediments (nominally the 154
upper ~ 1m) that are most effectively sampled with shallow coring systems designed to recover 155
an intact sediment-water interface (e.g., hydraulically-damped multicorer, box corer). The 156
rationale is because of the focus on processes associated with deposition, early diagenesis, and 157
burial of organic matter, rather than on down-core investigations used for paleooceanographic 158
and paleoclimate reconstruction. Sediment depth profile data primarily used to examine 159
diagenetic profiles, and to constrain sedimentation rates, mixed layer depths, redox gradients, 160
as well as to determine carbon fluxes and inventories.
161 162
2.1.2 Scope of data acquisition 163
The data currently comprising the MOSAIC database was extracted from over two hundred 164
publications. No unpublished data is included in the on-line version, and the focus of the 165
database in this initial phase of implementation is on an initial suite of commonly measured 166
sediment parameters (e.g. sampling depth, carbon content and δ13C) that are available in high 167
abundance. A non-exhaustive list of the most important parameters cataloged in the MOSAIC 168
database can be found in Table 1. A more comprehensive list of parameters that are targeted 169
for inclusion in the near future can be found in the Supplemental Information (SI).
170 171
2.1.3 Core parameters 172
The database was established based on selected key parameters, with a particular emphasis on 173
the radiocarbon content of OC, as well as other basic properties that provide broader 174
geochemical and sedimentological context (Table 1). The former include total organic carbon 175
(TOC) and total nitrogen (TN) content, organic carbon/total N ratios, and the stable carbon 176
isotopic composition (d13C and 14C values) of OC. Sedimentological parameters are yet to be 177
implemented in the on-line version but will include parameters such as grain size, mineral 178
specific surface area, mixed layer depth, oxygen penetration depth, sedimentation rate, porosity 179
and dry bulk density.
180 181
2.2 MOSAIC Structure 182
The normalized relational database structure of the MOSAIC database was created using the 183
open-source MySQL software (MySQL Workbench Community for Ubuntu 18 version 184
6.3.10). The relational aspect of the database means that data (e.g., related to sample or 185
location-specifics) are stored in data tables which are connected (or related) by a unique 186
identifier. “Normalized” implies that in the structure of the database redundancies are 187
eliminated (e.g., a variable such as water depth occurs only once in the database, Codd, 1990).
188
A schematic of the detailed database structure can be found in SI Figure 2. The database 189
structure contains entries for key geochemical parameters pertaining to ocean sediment core 190
samples, including organic matter content, isotopic signature, and composition, as well as 191
texture and sedimentological parameters. Information can be collected for bulk samples as well 192
as for example size and density fractions. Furthermore, it is designed to enable additional 193
modules that can accommodate data related to other sample suites such as sinking particulate 194
matter from the ocean water column (e.g., time-series sediment traps), or riverine samples. It 195
includes is an exclusivity option which can be used to indicate if data is in the public domain 196
or not (e.g., pending publication of separate contributions).
197
Reporting conventions are detailed in the SI Table 2. Units as specified in the original papers 198
were used (listed in SI). Where possible 14C information was collected as D14C, alternatively it 199
was collected as Fm and all D14C values were converted to Fm (Stuiver and Polach, 1977).
200
Ongoing efforts are underway to further harmonize the data and convert all data to D14C for 201
the next iteration for the MOSAIC database.
202
2.3 The MOSAIC Pipeline 203
There is a five-step pipeline for incorporation of data into MOSAIC. These are: (1) data 204
ingestion, (2) quality control, (3) transformation and structuring and (4) addition to a user- 205
friendly MySQL database interface, which is (5) available for users via a website (Figure 1).
206
This design enables users to query the collected data and augment and extend the existing 207
database using familiar spreadsheet software (Microsoft Excel®, LibreOffice). The associated 208
app allows any user to interactively select, visualize and query data without using database 209
(SQL) syntax (SI Figure 1).
210 211
2.3.1 Data ingestion 212
Input of data to the database is possible by filling in a pre-structured spreadsheet file with set 213
vocabularies. The user selects relevant parameter inputs from drop-down menus that streamline 214
data entry and assist in execution of subsequent SQL queries. Excel files were designed for 215
specific datasets, and within each Excel file there are three sub-tabs corresponding to groups 216
of the normalized MOSAIC SQL database (more details on database structure are provided in 217
the database). These tabs are (i) sample-related tab, (ii) geopoint-related tab (i.e., location), (iii) 218
author-related tab (i.e., paper). Certain variables pertaining to sample coordinates and depth 219
are required for data submission (i.e., latitude, longitude, water depth and sample core depth).
220
In this first version of MOSAIC, filled-in spreadsheet files with specified units and pre-defined 221
lists can be sent to mosaic@erdw.ethz.ch1 for ingestion into the database.
222 223
2.3.2 Data quality control 224
Quality control of the input data is implemented via a python script tailored to the pre-defined 225
spreadsheet files. This script auto-checks the values of key parameters such as latitude, 226
longitude, carbon and nitrogen content, 13C, 14C, CaCO3 content, SiO2 content and sediment 227
texture-related parameters. The auto-check produces a log file with flags for unexpected values.
228
In turn, the flags point to the exact line containing possible out-of-bound values. For example, 229
for TOC (%), if values are negative, there will be a prompt “cannot be negative, please check”, 230
when values are > 2 and <20 there is a prompt “is quite high. Are you sure it is correct?” and 231
lastly if values are > 20 there is the prompt “value is high. Please check units”. Each flag is 232
accompanied by a line number to locate the possibly erroneous data. These flags then trigger a 233
manual quality check of the data by an expert in-house user.
234 235
2.3.3 Data transformation and structuring 236
The next step involves transforming data (using Python code) from Excel into csv files that are 237
compatible with the normalized relational database structure in SQL. This is done by (i) adding 238
unique identifiers to the data and (ii) transforming the data into appropriate csv files.
239
Importantly for the database structure, unique identifiers are created for each appropriate 240
database table (SI Figure 2). For example, for a specific location, an individual sediment core 241
may yield multiple samples (i.e., core sections corresponding to different depth intervals), with 242
1 Data ingestion files MOSAIC_data_input_file.xlsx or MOSAIC_data_input_file.ods are available with this publication
multiple measurements (e.g., C, C and %TOC) performed on each sample (section). In this 243
example, the location is assigned a unique geopoint location identifier, the core receives a 244
unique identifier, and each sample (section) is given a unique identifier. These identifiers 245
resurface in each database table (e.g., on compositional parameters), resulting in the possibility 246
of multiple cores and multiple sample identifiers for a single geopoint. For the creation of 247
identifiers, the Python script finds a unique combination of coordinates (i.e., latitude and 248
longitude), assigns an identifier and eliminates duplicates. It repeats this for all primary keys 249
in the database.
250 251
2.3.4 MySQL interface 252
The Excel files designed for facile data ingestion are transformed in order to be compatible 253
with the normalized database using a Python script. This script executes this transformation by 254
auto-creating the compatible csv files, including the unique identifiers for the primary keys.
255
The script can be adapted to a dataset and is provided in the SI. The MOSAIC SQL database 256
allows for a direct upload of csv following data quality assessment, addition of identifiers and 257
creation of csv files. At present, a member of the ETH Biogeoscience group is allocated to 258
undertake this task upon receipt of files.
259 260
2.3.5 MOSAIC Website: User access and citing of data 261
The website (mosaic.ethz.ch) can be cited using the digital object identifier number (DOI) 262
https://doi.org/10.5168/mosaic019.1. In order to access data, users do not need to use SQL 263
syntax. Instead, users can select data of interest using drop-down menus or by selecting data 264
via a visual geographic interface. The selected data resulting from the query is shown in a table 265
and can be directly downloaded as a csv file (SI Figure 1). When querying data through the 266
MOSAIC website, the relational aspects of the database ensures that, for example, when a 267
certain location is selected, all data pertaining to this point appear in the table and are 268
downloaded. For users versed in SQL syntax, all accompanying data is available in SQL code, 269
which can be imported in both MySQL and PostgreSQL graphic user interface software. In 270
this format, all data can be queried in using SQL syntax.
271
3. Results and Discussion 272
3.1 Excerpts from the MOSAIC database 273
We provide examples of information extracted from MOSAIC (https://doi.org/10.5168/mo- 274
saic019.1, Van der Voort et al., 2019). The intention here is to illustrate broad-scale variability 275
in OC properties rather that offer in-depth interpretations. The latter will be the focus of 276
subsequent contributions.
277
We first explore the statistical distributions of geochemical properties (Figure 3). On a 278
global scale, TOC contents of marine surface sediments (< 100 cm) are lognormally distributed 279
around ~1 % (mean = 1.63%, median = 1.14%; n= 8688; Figure 3a), consistent with prior 280
observations (Keil, 2017; Seiter et al., 2004, 2005). The distribution of stable carbon isotope 281
(δ13C) values of OC shows two distinct populations (mean = -22.6‰, median = -22.18‰; n = 282
4297; Figure 3b), likely reflecting relative dominance of terrestrial C3 plant (~-27 ‰) and 283
marine (~-22 ‰) sources (Burdige, 2005; Sackett and Thomson, 1963). Corresponding 284
radiocarbon contents (expressed here as Fm values) exhibit a more unimodal distribution with 285
an average Fm value of ~0.7 (Mean = 0.7, Median = 0.73, n = 709; Figure 3c), highlighting the 286
significant proportions of pre-aged OC in globally distributed marine surficial sediments 287
(Griffith et al 2010).
288
Carbon isotopic compositions of surface sediment OC exhibits substantial variability 289
when plotted as a function of water depth (Figure 4). Radiocarbon contents are especially 290
variable and generally lower in shallow (coastal) areas where TOC is also relatively low 291
(Figure 4a). Coastal areas are both prone to supply of pre-aged OC from adjacent land masses 292
(e.g. Tao et al., 2015; van der Voort et al., 2017), as well as ageing associated with sediment 293
reworking by bottom currents (Bao et al., 2016). A similar pattern of variability is evident in 294
δ13C values (Figure 4b) which exhibit a larger spread on continental shelves (~-13 to -30 ‰) 295
and converge towards higher (more 13C-enriched) δ13C values (~- 22 ‰) in the deeper ocean.
296
These trends reflect trajectories and modes carbon supply both from land and the ocean to the 297
seafloor that govern OC sequestration and resulting sedimentary signatures (Bianchi et al., 298
2007; Burdige, 2005). Distinguishing between and quantifying the relative importance these 299
factors is important for understanding consequences for carbon burial (Arndt et al., 2013; Bao 300
et al., 2019; Bao et al., 2016), and requires ancillary geochemical and sedimentological (e.g., 301
biomarker signatures, grain size distributions) information that will be incorporated into a 302
future iteration of the MOSAIC database.
303
Broad-scale variability in OC characteristics of surface marine sediments also emerges 304
when properties are examined as a function of latitude (Figure 5). For example, despite 305
considerable scatter in stable carbon isotopic compositions, there is a general trend from higher 306
to lower d13C values with increasing latitude (Figure 5a). This could reflect latitudinal 307
variations in the carbon isotopic composition of marine phytoplankton (Goericke and Fry, 308
1994), and/or changes in the proportions and d13C values of terrestrial OC inputs (e.g., balance 309
of C3 vs C4 vegetation; Huang et al., 2000). Latitudinal trends in 14C are less clear due to a 310
paucity of data with sufficient geographic coverage (Figure 5b), and serve to highlight ocean 311
regions and domains that are presently understudied with respect to this and other sediment 312
variables.
313 314
3.2 Scientific value of MOSAIC 315
The compilation of data and subsequent re-analyses holds the potential to yield novel insights 316
into the distribution and composition of OC accumulating in the contemporary marine 317
environment, shed light on underlying processes, and identify gaps in existing data sets. The 318
latter is particularly pertinent for 14C data and ancillary measurements necessary to broadly 319
apply isotopically-enabled models of organic turnover and burial in sediments (e.g., Griffith 320
et al., 2010) and constrain geographic variability in the age distribution of sedimentary OC in 321
an analogous fashion to those of, for example, soil carbon (e.g. Shi et al., 2020). Filling such 322
gaps is also important given increasing interest in developing robust assessments of carbon 323
stocks in coastal marine sediments in the context of future greenhouse gas reporting protocols 324
(e.g. Avelar et al., 2017). Moreover, regional-scale data compilation of spatially 325
comprehensive geochemical and sedimentological information (Bao, et al., 2018; Bao et al., 326
2016), coupled the application of novel numerical clustering methods (Van der Voort et al., 327
2018) can facilitate refinement of criteria for delineating biogeochemically provinces 328
(Longhurst, 2007; Seiter et al., 2004), that reflect both source inputs and hydrodynamic 329
regimes, in order to improve carbon cycle budgets and models. Such examples highlight the 330
value of leveraging existing datasets, connecting various data sources and using other types of 331
analyses (modelling, statistics) in order to garner new insights into underlying processes.
332 333
3.3 MOSAIC in context.
334
MOSAIC complements other ongoing efforts to collect and organize a broad spectrum 335
geochemical and related data, such as the PANGAEA data repository (AWI and MARUM, 336
2020), as well as those with more targeted missions, such as the International Soil Radiocarbon 337
Database (ISRaD; Lawrence et al., 2020). It differs from these and other initiatives in its 338
targeted approach with a primary focus on (i) collating data pertinent to OC burial on 339
continental margins, (ii) upper sediment layers (nominally < ~ 1m) that encompass early 340
diagenetic processes and recent deposition, and (iii) radiocarbon information that bridges to 341
equivalent databases for other carbon cycle compartments. The MOSAIC database has been 342
designed to be modular and adaptable to accommodate further developments and expansion of 343
its dimensionality, while retaining its overall carbon-centric focus. In particular, inclusion of 344
14C data on specific fractions separated, for example, according to sediment density 345
(Wakeham et al., 2009) or thermal lability (Rosenheim et al., 2008), or at the molecular level 346
(e.g. Druffel et al., 2010). In this context, it is anticipated that MOSAIC will serve as a key 347
research and teaching resource for biogeochemists focusing on contemporary biogeochemical 348
processes as well as seeking to interrogate sedimentary archives to develop records of past 349
oceanographic conditions.
350 351
4. Data Availability 352
The data of the database can be accessed via mosaic.ethz.ch and the DOI is 353
https://doi.org/10.5168/mosaic019.1 (Van der Voort et al., 2019). Users who would like to add 354
data to the database can fill in the data in the Excel® templates that can be found in the SI of 355
this paper and send it to mosaic@erdw.ethz.ch.
356 357
5. Conclusion and Outlook 358
In this paper, we introduce the motivation for development of a database (MOSAIC) focused 359
on OC accumulating in contemporary continental margin sediments. The structure of the 360
database and the associated web interface for data submission and retrieval is presented. The 361
supporting infrastructure was built with open-source software (SQL, R, Python, LibreCalc;
362
also provided with this contribution). Current data residing within MOSAIC derives from over 363
200 peer-reviewed papers, with the intention that this resource will further expand both 364
regarding data density and dimensionality, with a specific emphasis on radiocarbon as an 365
underdetermined yet crucial property for constraining carbon cycle processes. Construction of 366
parallel databases focused on riverine data and ocean sediment trap data are also under 367
development.
368
6. Video Supplement 369
Accompanying this paper is a short instructional video (in SI) which explains users how to 370
download the data from MOSAIC (https://doi.org/10.5168/mosaic019.1, Van der Voort et al., 371
2019).
372 373
7. Author Contributions 374
Tim Eglinton led the conceptual development of the MOSAIC project. Tessa Sophia van der 375
Voort designed, structured and filled the SQL database and also created the associated 376
infrastructure in R, Python and Excel/LibreOffice. Thomas M. Blattmann and Daniel 377
Montluçon provided feedback on the database structure and website development and 378
contributed to discussion of the data. Mohammed Usman collected the MOSAIC data and 379
contributed to the data evaluation. Thomas Loeffler enabled the set-up of infrastructure and 380
contributed to the technical components of the paper. Maria Luisa Tavagnacontributed to the 381
concept development. Nicolas Gruber contributed to the MOSAIC concept development and 382
project set-up. T.S. van der Voort prepared the manuscript with help of all co-authors.
383 384
8. Competing interests 385
All co-authors declare that they have no competing interests regarding this manuscript.
386 387
9. Acknowledgements 388
This project was funded by the ETH project (T. Eglinton and N. Gruber) "Elucidating processes 389
that govern carbon burial in the global ocean” (46 15-1). We thank Melissa Schwab for sharing 390
her insights in optimal R visualization. Many thanks also to Stephane Beaussier, who helped 391
to overcome numerous challenges in the development of this project. We thank Anastasiia 392
Ignatova for contributions to a prototype of MOSAIC. We thank Philip Pika for his insights 393
into sediment parameters.
394 395
10. Tables and Figures 396
397
Table 1 Overview of key variables and their abundance in the MOSAIC database. An exhaustive list can be found in the SI.
398
Main variable Unit Number of
datapoints
Required (Y/N)
Geopoints Latitude Degrees (°) 8706 Y
Longitude Degrees (°) 8706 Y
Samples Ocean Exclusivity Clause Y/N 8706 Y
Water depth m 4297 Y2
Sample core depth (average)
Centimeter (cm) 7147 Y
Sample name VARCHAR - N
Total Organic Carbon (TOC)
Percentage (%) 8688 N
d13C Permil (‰) 4297 N
Fm fraction 709 N
C:N Ratio Ratio 504 N
SiO2 Percentage (%) 370 N
CaCO3 Percentage (%) 1668 N
Articles Article doi VARCHAR 235 N
2 There are ongoing efforts to collect all water depth information, ancillary information will be attained using the GEBCO bathymetric grid (GEBCO, 2020).
399
400
Figure 1 Overview of the MOSAIC pipeline. Data ingestion (1) is done with excel-based input files. Then, (2) data quality control
401
is achieved using is a python script which auto-checks the data for outliers and produces a subsequent log. Afterwards, (3)
402
unique identifiers are added and the data is transformed into SQL-compatible format in Python. Subsequently, (4) data
403
addition to the MOSAIC database occurs within the MySQL GUI, and finally (5), the data is auto-updated within the R
404
environment and the Rshiny app is updated.
405
406
407 Figure 2 distribution of all datapoints across the globe (a) from a standard projection and (b) from a polar-centric projection.
408 Colours indicate TOC content (%).
409
−60
−30 0 30 60
−180 −135 −90 −45 180
0 30 60
longitude
0.01 0.1 1 10 TOC (%)
longitude
0 45 90 135
latitude
(a) (b)
410
411
Figure 3 Distribution of data for key sedimentary parameters included in MOSAIC: (a) TOC shows a log-normal distribution
412
which peaks at ~1.1 % and averages around 1.6 %, (b) δ13C values show two distinct peaks at ~-22 and ~27 permill. (c)
413
radiocarbon shows a strongly depleted signature with the fraction modern value averaging at ~0.7. The (d) C:N ratio global
414
average is ~ 10. The median (e) silicate (SiO2) and (f) carbonate (CaCO3) contents are ~14%, and ~ 13%, respectively
415
0 300 600 900
0.01 0.1
TOC (%)
0 100 200 300 400 500
−40 −30 −20 −10
�13C
0 50 100 150
0.0 0.3 0.6 0.9
Fm
0 50 100 150
0 10 20 30
C:N Ratio
0 20 40
0 25 50 75 100
SiO2 (%)
0 50 100 150 200
0 25 50 75 100
CaCO3 (%) 1st Qu. : 0.61
median : 1.14 mean : 1. 63 3rd Qu. : 2.02 n = 8688
1st Qu. : -24.15 median : -22.18 mean : -22.60 3rd Qu. : -20.94 n = 4297
1st Qu. : 0.64 median : 0.73 mean : 0.70 3rd Qu. : 0.78 n = 709
1st Qu. : 8.7 median : 9.4 mean : 10.4 3rd Qu. : 11.7 n = 504
1st Qu. : 4.2 median : 14.2 mean : 26.4 3rd Qu. : 53.3 n = 370
1st Qu. : 4.5 median : 13.1 mean : 21.2 3rd Qu. : 29.8 n = 1668
1 10
(a) (b) (c)
(d) (e) (f)
416
417
Figure 4 (a) Fraction modern versus depth, bubble size and colour indicate sample TOC content (%). On ocean shelves (shallow
418
depths) we observe generally low TOC values and depleted Fm values. Carbon in deeper oceans show a larger spread in ages
419
and TOC content. (b) δ13C modern versus depth, bubble size and colour indicate sample TOC content (%). On ocean shelves
420
(shallow depths) we observe a large spread in ∂13C values. Carbon in deeper oceans show a smaller spread and converge to
421
less depleted δ13C values.
422
0
1000
2000
3000
4000
5000
0.00 0.25 0.50 0.75 1.00 Fm
Depth (m)
−30 −25 −20 −15 δ13C
0.01 0.1 1 10 TOC (%)
(a) (b)
n=709 n=4297
423
Figure 5 latitude (a) versus δ13C and (b) Fraction Modern (Fm), colour indicated by TOC content (%). The δ13C tends to be less
424
depleted in the low-latitudes. The Fm shows a sampling bias in the mid-range latitudes and also appears to be less depleted
425
in the lower latitudes.
426 427
−30
−25
−20
−15
0.00 0.25 0.50 0.75 1.00
−50 0 50
latitude
Fm
−30
−25
−20
−15
0.00 0.25 0.50 0.75 1.00
−50 0 50
latitude
�13 CFm 0.01
0.1 1 10 TOC (%) (a)
(b)
n=709 n=4297
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